ePrints.FRI - University of Ljubljana, Faculty of Computer and Information Science

Active learning

Vito Janko (2012) Active learning. EngD thesis.

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    In contrast with standard supervised learning where learner gets random training examples, an active learner can pick training examples itself. Examples picked this way can be more ``informative" and we show that for the same model we often need less of them. In this work we present ways in which a learner can choose examples and criteria on how to evaluate their ``informativness". We compare different approaches and show that active learning can surpass the standard one. We show some theoretical foundations of active learning and give some criteria that guarantee its success. At the end we present results of our own tests.

    Item Type: Thesis (EngD thesis)
    Keywords: Machine learning, active learning, classification, classification error, query, training sample
    Number of Pages: 84
    Language of Content: Slovenian
    Mentor / Comentors:
    Name and SurnameIDFunction
    prof. dr. Igor Kononenko237Mentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=50070&select=(ID=00009462612)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 1843
    Date Deposited: 22 Sep 2012 15:49
    Last Modified: 24 Oct 2012 09:43
    URI: http://eprints.fri.uni-lj.si/id/eprint/1843

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